Artificial Intelligence I : Intelligent Agents
Lecturer: Tom LenaertsInstitut de Recherches Interdisciplinaires et de Développements en Intelligence Artificielle (IRIDIA)Université Libre de Bruxelles
TLo (IRIDIA) 2May 1, 2023
Outline Agents and environments.
The vacuum-cleaner world The concept of rational behavior. Environments. Agent structure.
TLo (IRIDIA) 3May 1, 2023
Agents and environments Agents include human, robots,
softbots, thermostats, etc. The agent function maps
percept sequence to actions
An agent can perceive its own actions, but not always it effects.
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f : P* → A
TLo (IRIDIA) 4May 1, 2023
Agents and environments
The agent function will internally be represented by the agent program.
The agent program runs on the physical architecture to produce f.
TLo (IRIDIA) 5May 1, 2023
The vacuum-cleaner world
Environment: square A and B Percepts: [location and content] e.g. [A, Dirty] Actions: left, right, suck, and no-op
TLo (IRIDIA) 6May 1, 2023
The vacuum-cleaner world
Percept sequence Action
[A,Clean] Right
[A, Dirty] Suck
[B, Clean] Left
[B, Dirty] Suck
[A, Clean],[A, Clean] Right
[A, Clean],[A, Dirty] Suck
… …
TLo (IRIDIA) 7May 1, 2023
The vacuum-cleaner world
function REFLEX-VACUUM-AGENT ([location, status]) return an actionif status == Dirty then return Suckelse if location == A then return Rightelse if location == B then return Left
What is the right function? Can it be implemented in a small agent program?
TLo (IRIDIA) 8May 1, 2023
The concept of rationality A rational agent is one that does the right thing.
Every entry in the table is filled out correctly. What is the right thing?
Approximation: the most succesfull agent. Measure of success?
Performance measure should be objective E.g. the amount of dirt cleaned within a certain time. E.g. how clean the floor is. …
Performance measure according to what is wanted in the environment instead of how the agents should behave.
TLo (IRIDIA) 9May 1, 2023
Rationality What is rational at a given time depends on four things:
Performance measure, Prior environment knowledge, Actions, Percept sequence to date (sensors).
DEF: A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date and prior environment knowledge.
TLo (IRIDIA) 10May 1, 2023
Rationality Rationality omniscience
An omniscient agent knows the actual outcome of its actions.
Rationality perfection Rationality maximizes expected performance, while
perfection maximizes actual performance.
TLo (IRIDIA) 11May 1, 2023
Rationality The proposed definition requires:
Information gathering/exploration To maximize future rewards
Learn from percepts Extending prior knowledge
Agent autonomy Compensate for incorrect prior knowledge
TLo (IRIDIA) 12May 1, 2023
Environments To design a rational agent we must specify its task
environment. PEAS description of the environment:
Performance Environment Actuators Sensors
TLo (IRIDIA) 13May 1, 2023
Environments E.g. Fully automated taxi:
PEAS description of the environment: Performance
Safety, destination, profits, legality, comfort Environment
Streets/freeways, other traffic, pedestrians, weather,, … Actuators
Steering, accelerating, brake, horn, speaker/display,… Sensors
Video, sonar, speedometer, engine sensors, keyboard, GPS, …
TLo (IRIDIA) 14May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable??
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??
TLo (IRIDIA) 15May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable??
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??
Fully vs. partially observable: an environment is full observable when the sensors can detect all aspects that are relevant to the choice of action.
TLo (IRIDIA) 16May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??
Fully vs. partially observable: an environment is full observable when the sensors can detect all aspects that are relevant to the choice of action.
TLo (IRIDIA) 17May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??
Deterministic vs. stochastic: if the next environment state is completelydetermined by the current state the executed action then the environment is
deterministic.
TLo (IRIDIA) 18May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic??
Static??
Discrete??
Single-agent??
Deterministic vs. stochastic: if the next environment state is completelydetermined by the current state the executed action then the environment is
deterministic.
TLo (IRIDIA) 19May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic??
Static??
Discrete??
Single-agent??
Episodic vs. sequential: In an episodic environment the agent’s experiencecan be divided into atomic steps where the agents perceives and then performs
A single action. The choice of action depends only on the episode itself
TLo (IRIDIA) 20May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static??
Discrete??
Single-agent??
Episodic vs. sequential: In an episodic environment the agent’s experiencecan be divided into atomic steps where the agents perceives and then performs
A single action. The choice of action depends only on the episode itself
TLo (IRIDIA) 21May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static??
Discrete??
Single-agent??
Static vs. dynamic: If the environment can change while the agent is choosingan action, the environment is dynamic. Semi-dynamic if the agent’s performance
changes even when the environment remains the same.
TLo (IRIDIA) 22May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete??
Single-agent??
Static vs. dynamic: If the environment can change while the agent is choosingan action, the environment is dynamic. Semi-dynamic if the agent’s performance
changes even when the environment remains the same.
TLo (IRIDIA) 23May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete??
Single-agent??
Discrete vs. continuous: This distinction can be applied to the state of the environment, the way time is handled and to the percepts/actions of the agent.
TLo (IRIDIA) 24May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete?? YES YES YES NO
Single-agent??
Discrete vs. continuous: This distinction can be applied to the state of the environment, the way time is handled and to the percepts/actions of the agent.
TLo (IRIDIA) 25May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete?? YES YES YES NO
Single-agent??
Single vs. multi-agent: Does the environment contain other agents whoare also maximizing some performance measure that depends on the
current agent’s actions?
TLo (IRIDIA) 26May 1, 2023
Environment types
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete?? YES YES YES NO
Single-agent?? YES NO NO NO
Single vs. multi-agent: Does the environment contain other agents whoare also maximizing some performance measure that depends on the
current agent’s actions?
TLo (IRIDIA) 27May 1, 2023
Environment types The simplest environment is
Fully observable, deterministic, episodic, static, discrete and single-agent.
Most real situations are: Partially observable, stochastic, sequential, dynamic,
continuous and multi-agent.
TLo (IRIDIA) 28May 1, 2023
Agent types How does the inside of the agent work?
Agent = architecture + program
All agents have the same skeleton: Input = current percepts Output = action Program= manipulates input to produce output
Note difference with agent function.
TLo (IRIDIA) 29May 1, 2023
Agent typesFunction TABLE-DRIVEN_AGENT(percept) returns an action
static: percepts, a sequence initially emptytable, a table of actions, indexed by percept sequence
append percept to the end of perceptsaction LOOKUP(percepts, table)return action
This approach is doomed to failure
TLo (IRIDIA) 30May 1, 2023
Agent types Four basic kind of agent programs will be
discussed: Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents
All these can be turned into learning agents.
TLo (IRIDIA) 31May 1, 2023
Agent types; simple reflex Select action on the basis
of only the current percept. E.g. the vacuum-agent
Large reduction in possible percept/action situations(next page).
Implemented through condition-action rules If dirty then suck
TLo (IRIDIA) 32May 1, 2023
The vacuum-cleaner world
function REFLEX-VACUUM-AGENT ([location, status]) return an actionif status == Dirty then return Suckelse if location == A then return Rightelse if location == B then return Left
Reduction from 4T to 4 entries
TLo (IRIDIA) 33May 1, 2023
Agent types; simple reflexfunction SIMPLE-REFLEX-AGENT(percept) returns an action
static: rules, a set of condition-action rules
state INTERPRET-INPUT(percept)rule RULE-MATCH(state, rule)action RULE-ACTION[rule]return action
Will only work if the environment is fully observable otherwise infinite loops may occur.
TLo (IRIDIA) 34May 1, 2023
Agent types; reflex and state To tackle partially
observable environments. Maintain internal state
Over time update state using world knowledge How does the world
change. How do actions affect
world. Model of World
TLo (IRIDIA) 35May 1, 2023
Agent types; reflex and statefunction REFLEX-AGENT-WITH-STATE(percept) returns an action
static: rules, a set of condition-action rulesstate, a description of the current world stateaction, the most recent action.
state UPDATE-STATE(state, action, percept)rule RULE-MATCH(state, rule)action RULE-ACTION[rule]return action
TLo (IRIDIA) 36May 1, 2023
Agent types; goal-based The agent needs a goal to know
which situations are desirable. Things become difficult when long
sequences of actions are required to find the goal.
Typically investigated in search and planning research.
Major difference: future is taken into account
Is more flexible since knowledge is represented explicitly and can be manipulated.
TLo (IRIDIA) 37May 1, 2023
Agent types; utility-based Certain goals can be reached in
different ways. Some are better, have a
higher utility. Utility function maps a
(sequence of) state(s) onto a real number.
Improves on goals: Selecting between conflicting
goals Select appropriately between
several goals based on likelihood of success.
TLo (IRIDIA) 38May 1, 2023
Agent types; learning All previous agent-programs
describe methods for selecting actions. Yet it does not explain the
origin of these programs. Learning mechanisms can be
used to perform this task. Teach them instead of
instructing them. Advantage is the robustness
of the program toward initially unknown environments.
TLo (IRIDIA) 39May 1, 2023
Agent types; learning Learning element: introduce
improvements in performance element. Critic provides feedback on agents
performance based on fixed performance standard.
Performance element: selecting actions based on percepts. Corresponds to the previous agent
programs Problem generator: suggests
actions that will lead to new and informative experiences. Exploration vs. exploitation